{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[15:44:13] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[15:44:14] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[15:44:14] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[15:44:15] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 52 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[15:44:16] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 54 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[15:44:16] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[15:44:17] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "#from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "if __name__ == '__main__':                  ##  这里如果不加这条主循环判断包装语句，代码会在网格搜索的fit语句处报一个关于并行计算的BUG\n",
    "                                            ##  最后经百度找到了这个解决方案，所以为了方便我把所有代码写在了同一个代码框里\n",
    "    #读取训练数据\n",
    "    train = pd.read_csv(\"RentListingInquries_FE_train.csv\")\n",
    "    # 取出特征与标签\n",
    "    y_train = train[\"interest_level\"].values\n",
    "    train = train.drop([\"interest_level\"], axis=1)\n",
    "    X_train = np.array(train)\n",
    "    \n",
    "    #max_depth 建议3-10， min_child_weight=1／sqrt(ratio_rare_event) 大致在3.5\n",
    "    max_depth = range(3,10,2)\n",
    "    min_child_weight = range(1,6,2)\n",
    "    param_test2_1 = dict(max_depth=max_depth, min_child_weight=min_child_weight)\n",
    "    param_test2_1\n",
    "    \n",
    "    # prepare cross validation\n",
    "    kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)\n",
    "    \n",
    "    xgb2_1 = XGBClassifier( \n",
    "         learning_rate =0.1,\n",
    "         n_estimators=608,  #第一轮参数调整得到的n_estimators最优值\n",
    "         max_depth=5,\n",
    "         min_child_weight=1,\n",
    "         gamma=0,\n",
    "         subsample=0.3,\n",
    "         colsample_bytree=0.8,\n",
    "         colsample_bylevel = 0.7,\n",
    "         objective= 'multi:softprob',\n",
    "         seed=3,\n",
    "         silent=0)\n",
    "    gsearch2_1 = GridSearchCV(xgb2_1, param_grid = param_test2_1, scoring='neg_log_loss',n_jobs=-1, cv=kfold)\n",
    "    gsearch2_1.fit(X_train , y_train)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 5, 'min_child_weight': 1}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " gsearch2_1.best_params_  #粗调得到的最优参数：最大深度为5，叶子节点最小样本权重和为1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[mean: -0.59926, std: 0.00292, params: {'max_depth': 3, 'min_child_weight': 1},\n",
       " mean: -0.59952, std: 0.00299, params: {'max_depth': 3, 'min_child_weight': 3},\n",
       " mean: -0.59944, std: 0.00323, params: {'max_depth': 3, 'min_child_weight': 5},\n",
       " mean: -0.58813, std: 0.00425, params: {'max_depth': 5, 'min_child_weight': 1},\n",
       " mean: -0.58896, std: 0.00400, params: {'max_depth': 5, 'min_child_weight': 3},\n",
       " mean: -0.58847, std: 0.00338, params: {'max_depth': 5, 'min_child_weight': 5},\n",
       " mean: -0.59106, std: 0.00310, params: {'max_depth': 7, 'min_child_weight': 1},\n",
       " mean: -0.59040, std: 0.00371, params: {'max_depth': 7, 'min_child_weight': 3},\n",
       " mean: -0.58981, std: 0.00409, params: {'max_depth': 7, 'min_child_weight': 5},\n",
       " mean: -0.60885, std: 0.00390, params: {'max_depth': 9, 'min_child_weight': 1},\n",
       " mean: -0.60355, std: 0.00539, params: {'max_depth': 9, 'min_child_weight': 3},\n",
       " mean: -0.59857, std: 0.00304, params: {'max_depth': 9, 'min_child_weight': 5}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch2_1.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5881333281807066"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch2_1.best_score_ #最佳分数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 530.59580007,  585.01100006,  593.53140001,  935.05980005,\n",
       "         895.58639989,  941.76780009, 1366.39739995, 1397.25299997,\n",
       "        1375.93219995, 1791.40780005, 1757.56039996, 1543.86700001]),\n",
       " 'mean_score_time': array([15.61839995, 20.40339999, 12.02940001, 14.13379993, 23.22680001,\n",
       "        14.84059997, 15.85020003, 14.89879999, 20.51820002, 22.58979998,\n",
       "        23.82400002, 16.59119997]),\n",
       " 'mean_test_score': array([-0.5992615 , -0.59951614, -0.5994386 , -0.58813333, -0.58896035,\n",
       "        -0.5884718 , -0.59106137, -0.59039744, -0.58981151, -0.60884628,\n",
       "        -0.60354884, -0.59857083]),\n",
       " 'mean_train_score': array([-0.57269462, -0.57386817, -0.57419441, -0.5001661 , -0.50656433,\n",
       "        -0.51018772, -0.38713335, -0.410633  , -0.42509775, -0.25662687,\n",
       "        -0.30542696, -0.33491137]),\n",
       " 'param_max_depth': masked_array(data=[3, 3, 3, 5, 5, 5, 7, 7, 7, 9, 9, 9],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_min_child_weight': masked_array(data=[1, 3, 5, 1, 3, 5, 1, 3, 5, 1, 3, 5],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'max_depth': 3, 'min_child_weight': 1},\n",
       "  {'max_depth': 3, 'min_child_weight': 3},\n",
       "  {'max_depth': 3, 'min_child_weight': 5},\n",
       "  {'max_depth': 5, 'min_child_weight': 1},\n",
       "  {'max_depth': 5, 'min_child_weight': 3},\n",
       "  {'max_depth': 5, 'min_child_weight': 5},\n",
       "  {'max_depth': 7, 'min_child_weight': 1},\n",
       "  {'max_depth': 7, 'min_child_weight': 3},\n",
       "  {'max_depth': 7, 'min_child_weight': 5},\n",
       "  {'max_depth': 9, 'min_child_weight': 1},\n",
       "  {'max_depth': 9, 'min_child_weight': 3},\n",
       "  {'max_depth': 9, 'min_child_weight': 5}],\n",
       " 'rank_test_score': array([ 8, 10,  9,  1,  3,  2,  6,  5,  4, 12, 11,  7]),\n",
       " 'split0_test_score': array([-0.59478605, -0.59483154, -0.59425596, -0.58029482, -0.58203892,\n",
       "        -0.58233462, -0.58560875, -0.58442293, -0.58335644, -0.60281977,\n",
       "        -0.59727582, -0.59390301]),\n",
       " 'split0_train_score': array([-0.57424078, -0.57556479, -0.57559939, -0.50114067, -0.5087983 ,\n",
       "        -0.5125472 , -0.3894005 , -0.4127558 , -0.42794278, -0.25798524,\n",
       "        -0.30667988, -0.33534274]),\n",
       " 'split1_test_score': array([-0.59848482, -0.59817931, -0.59829377, -0.58723939, -0.58691   ,\n",
       "        -0.58811675, -0.59030281, -0.58938168, -0.58780881, -0.60663818,\n",
       "        -0.59812542, -0.59810292]),\n",
       " 'split1_train_score': array([-0.57326158, -0.57396745, -0.57461411, -0.50080269, -0.5073554 ,\n",
       "        -0.51007898, -0.38714471, -0.41079589, -0.42438721, -0.2544797 ,\n",
       "        -0.30482126, -0.33495998]),\n",
       " 'split2_test_score': array([-0.59869033, -0.59929206, -0.59899346, -0.58989426, -0.59148009,\n",
       "        -0.58875403, -0.59172911, -0.59268946, -0.59011548, -0.61126114,\n",
       "        -0.60413511, -0.59956899]),\n",
       " 'split2_train_score': array([-0.5727665 , -0.57388223, -0.57369923, -0.49978872, -0.50593683,\n",
       "        -0.51019817, -0.38596459, -0.40911298, -0.42407026, -0.25574066,\n",
       "        -0.30567942, -0.33400293]),\n",
       " 'split3_test_score': array([-0.60369711, -0.60343763, -0.60353864, -0.59114031, -0.59162622,\n",
       "        -0.59150985, -0.59282951, -0.5899217 , -0.59240026, -0.60929209,\n",
       "        -0.60640507, -0.59792897]),\n",
       " 'split3_train_score': array([-0.57233315, -0.57362564, -0.57419051, -0.50087883, -0.50661324,\n",
       "        -0.51014326, -0.38598287, -0.40965445, -0.42349968, -0.25589909,\n",
       "        -0.30465002, -0.33513045]),\n",
       " 'split4_test_score': array([-0.60064963, -0.60184086, -0.602112  , -0.59209907, -0.59274767,\n",
       "        -0.59164469, -0.59483782, -0.59557301, -0.59537823, -0.61422187,\n",
       "        -0.61180528, -0.6033517 ]),\n",
       " 'split4_train_score': array([-0.57087108, -0.57230075, -0.5728688 , -0.49821959, -0.50411787,\n",
       "        -0.507971  , -0.38717409, -0.41084586, -0.42558883, -0.25902967,\n",
       "        -0.30530423, -0.33512072]),\n",
       " 'std_fit_time': array([ 36.69283782,  47.33504084,  21.92462613,  44.51383382,\n",
       "         45.85757076,  35.40693574,  12.20026048,  18.68032989,\n",
       "         17.05588781,  22.27578012,  36.68068622, 180.69107521]),\n",
       " 'std_score_time': array([ 7.3603594 ,  8.87011655,  4.60992781,  4.97740306, 10.89944262,\n",
       "         3.66350765,  3.88713209,  2.93643166,  8.25210712,  3.46804463,\n",
       "         3.15379813, 11.80052221]),\n",
       " 'std_test_score': array([0.00291763, 0.00298551, 0.00323503, 0.00424528, 0.00399776,\n",
       "        0.00338136, 0.00310286, 0.00371453, 0.00408551, 0.00390173,\n",
       "        0.00539301, 0.00304199]),\n",
       " 'std_train_score': array([0.00111114, 0.00103963, 0.00091133, 0.00107651, 0.00154905,\n",
       "        0.00144879, 0.00125151, 0.00125269, 0.00157796, 0.00164584,\n",
       "        0.00072376, 0.00047022])}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch2_1.cv_results_  #全部结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: -0.588133 using {'max_depth': 5, 'min_child_weight': 1}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Log Loss')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (gsearch2_1.best_score_, gsearch2_1.best_params_))\n",
    "test_means = gsearch2_1.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = gsearch2_1.cv_results_[ 'std_test_score' ]\n",
    "train_means = gsearch2_1.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = gsearch2_1.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "pd.DataFrame(gsearch2_1.cv_results_).to_csv('my_preds_maxdepth_min_child_weights_1.csv')\n",
    "\n",
    "# plot results\n",
    "test_scores = np.array(test_means).reshape(len(max_depth), len(min_child_weight))\n",
    "train_scores = np.array(train_means).reshape(len(max_depth), len(min_child_weight))\n",
    "\n",
    "for i, value in enumerate(max_depth):\n",
    "    pyplot.plot(min_child_weight, -test_scores[i], label= 'test_max_depth:'   + str(value))\n",
    "#for i, value in enumerate(min_child_weight):\n",
    "#    pyplot.plot(max_depth, train_scores[i], label= 'train_min_child_weight:'   + str(value))\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'max_depth' )                                                                                                      \n",
    "pyplot.ylabel( 'Log Loss' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
